1. Field
The present disclosure relates generally to processing, and more specifically to techniques for performing transforms on data.
2. Background
Transforms are commonly used to convert data from one domain to another domain. For example, discrete cosine transform (DCT) is commonly used to transform data from spatial domain to frequency domain, and inverse discrete cosine transform (IDCT) is commonly used to transform data from frequency domain to spatial domain. DCT is widely used for image/video compression to spatially decorrelate blocks of picture elements (pixels) in images or video frames. The resulting transform coefficients are typically much less dependent on each other, which makes these coefficients more suitable for quantization and encoding. DCT also exhibits energy compaction property, which is the ability to map most of the energy of a block of pixels to only few (typically low order) transform coefficients. This energy compaction property can simplify the design of encoding algorithms.
Transforms such as DCT and IDCT may be performed on large quantity of data. Hence, it is desirable to perform transforms as efficiently as possible. Furthermore, it is desirable to perform computation for transforms using simple hardware in order to reduce cost and complexity.
There is therefore a need in the art for techniques to efficiently perform transforms on data.